Oops, I just noticed that I forgot to blog about one recent Effective Concurrency column: “Writing Lock-Free Code: A Corrected Queue” which also appeared in the October 2008 print issue of Dr. Dobb’s Journal.

From the article:

As we saw last month [1], lock-free coding is hard even for experts. There, I dissected a published lock-free queue implementation [2] and examined why the code was quite broken. This month, let’s see how to do it right.

Here is the complete-as-of-this-writing set of links to the published Effective Concurrency columns. As always, the months reflect the magazine print issue dates; they usually hit the web a bit sooner:

August 2007: The Pillars of Concurrency

September 2007: How Much Scalability Do You Have or Need?

October 2007: Use Critical Sections (Preferably Locks) to Eliminate Races

November 2007: Apply Critical Sections Consistently

December 2007: Avoid Calling Unknown Code While Inside a Critical Section

January 2007: Use Lock Hierarchies to Avoid Deadlock

February 2008: Break Amdahl’s Law!

March 2008: Going Superlinear

April 2008: Super Linearity and the Bigger Machine

May 2008: Interrupt Politely

June 2008: Maximize Locality, Minimize Contention

July 2008: Choose Concurrency-Friendly Data Structures

August 2008: The Many Faces of Deadlock

September 2008: Lock-Free Code: A False Sense of Security

October 2008: Writing Lock-Free Code: A Corrected Queue

November 2008: Writing a Generalized Concurrent Queue

December 2008: Understanding Parallel Performance

Wow, DDJ just posted the previous one a few days ago, and already the next Effective Concurrency column is available: “Understanding Parallel Performance” just went live, and will also appear in the print magazine.

From the article:

Let’s say that we’ve slickly written our code to apply divide-and-conquer algorithms and concurrent data structures and parallel traversals and all our other cool tricks that make our code wonderfully scalable in theory. Question: How do we know how well we’ve actually succeeded? Do we really know, or did we just try a couple of tests on a quad-core that looked reasonable and call it good? What key factors must we measure to understand our code’s performance, and answer not only whether our code scales, but quantify how well under different circumstances and workloads? What costs of concurrency do we have to take into account?

This month, I’ll summarize some key issues we need to keep in mind to accurately analyze the real performance of our parallel code. I’ll list some basic considerations, and then some common costs. Next month, I have a treat in store: We’ll take some real code and apply these techniques to analyze its performance in detail as we successively apply a number of optimizations and measure how much each one actually buys us, under what conditions and in what directions, and why.

I hope you enjoy it. Finally, here are links to previous Effective Concurrency columns (based on the magazine print issue dates):

August 2007: The Pillars of Concurrency

September 2007: How Much Scalability Do You Have or Need?

October 2007: Use Critical Sections (Preferably Locks) to Eliminate Races

November 2007: Apply Critical Sections Consistently

December 2007: Avoid Calling Unknown Code While Inside a Critical Section

January 2007: Use Lock Hierarchies to Avoid Deadlock

February 2008: Break Amdahl’s Law!

March 2008: Going Superlinear

April 2008: Super Linearity and the Bigger Machine

May 2008: Interrupt Politely

June 2008: Maximize Locality, Minimize Contention

July 2008: Choose Concurrency-Friendly Data Structures

August 2008: The Many Faces of Deadlock

September 2008: Lock-Free Code: A False Sense of Security

October 2008: Writing Lock-Free Code: A Corrected Queue

November 2008: Writing a Generalized Concurrent Queue

December 2008: Understanding Parallel Performance